Towards Domain-independent Biases for Action Selection in Robotic Task-planning under Uncertainty

Juan Carlos Saborío, Joachim Hertzberg

Abstract

Task-planning algorithms for robots must quickly select actions with high reward prospects despite the huge variability of their domains, and accounting for the high cost of performing the wrong action in the “real-world”. In response we propose an action selection method based on reward-shaping, for planning in (PO)MDP’s, that adds an informed action-selection bias but depends almost exclusively on a clear specification of the goal. Combined with a derived rollout policy for MCTS planners, we show promising results in relatively large domains of interest to robotics.

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Paper Citation


in Harvard Style

Saborío J. and Hertzberg J. (2018). Towards Domain-independent Biases for Action Selection in Robotic Task-planning under Uncertainty.In Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-275-2, pages 85-93. DOI: 10.5220/0006578500850093


in Bibtex Style

@conference{icaart18,
author={Juan Carlos Saborío and Joachim Hertzberg},
title={Towards Domain-independent Biases for Action Selection in Robotic Task-planning under Uncertainty},
booktitle={Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2018},
pages={85-93},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006578500850093},
isbn={978-989-758-275-2},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Towards Domain-independent Biases for Action Selection in Robotic Task-planning under Uncertainty
SN - 978-989-758-275-2
AU - Saborío J.
AU - Hertzberg J.
PY - 2018
SP - 85
EP - 93
DO - 10.5220/0006578500850093